Orthogonal frequency division multiplexing (OFDM) has been widely adopted for a variety of wireless communication standards, such as WLAN IEEE 802.11 a/g, DAB, TDMB, DVB-T, WiMax, and also 3GPP LTE. Due to its potential for low complexity receiver implementation, OFDM is particularly attractive for high-data rate transmission.
In OFDM, the transmission bandwidth is split into equidistantly spaced orthogonal sub-bands of identical width. Orthogonality is maintained under the prerequisite that the duration of the channel impulse response does not exceed the duration of the guard interval, and that the radio propagation channel conditions vary slowly enough. Both requirements are satisfied by proper selection of system parameters, such as sub-carrier spacing and guard interval duration. Then transmission of one data symbol is described by the simple equationyk,l=hk,l·xk,l+nk,l·  (1),wherein x is a transmitted symbol, h is a complex fading coefficient, n is a random noise sample, y is the corresponding received symbol, k is the OFDM subcarrier index, and l is the OFDM symbol index. The noise sample is characterized by the noise variance σn2. With different values for all different pairs of (k,l), this equation holds for all symbols in the time-frequency plane, as illustrated in FIG. 1.
An extension to OFDM with one TX antenna is multiple-input multiple-output (MIMO) OFDM, where multiple antennas are used both on the transmit side and the receive side. In this case, each element in the time-frequency plane corresponds to the equationyk,l=Hk,l·xk,l+nk,l,  (2)where x is a vector of transmitted symbol, H is a matrix of complex fading coefficients, n is a random noise sample vector, y is the corresponding received symbol vector. The random noise vector is characterized by its covariance matrix Φnn.
In LTE each element in the time-frequency plane is referred to as a resource element, and the entire time-frequency plane is divided into so-called resource blocks, which are rectangles of 12 subcarriers in frequency direction times 6 or 7 (depending on the cyclic prefix duration mode) OFDM symbols in time direction, as illustrated in FIG. 2.
LTE supports a number of link adaptation methods, in order to provide a certain desired quality of service to mobile users. The modulation and coding scheme, i.e., the modulation alphabet and the coding rate are adapted to given link conditions, in order to meet a targeted maximum packet error rate.
A mobile station receiving a signal from a base station experiences time/frequency varying channel conditions. In order to get the maximum throughput from a base-station, it is necessary to adjust the modulation and coding schemes (QPSK, 16-QAM, or 64-QAM) to the actual channel quality. To do so, mobile receivers are required to probe radio channel conditions and to feed back a limited set of estimated parameters, namely a channel quality indicator (CQI) that reflects the achievable spectral efficiency, and a channel rank indication (RI) and a precoding matrix index (PMI) both of which are needed to achieve that spectral efficiency.
To meet a target maximum packet error rate, the mobile station sends a proposal for the choice of the modulation and coding scheme to the base station (channel quality indication—CQI). In addition, there is a MIMO transmission mode utilizing implicit beamforming via precoding, which allows improved utilization of the spatial channel dimension. In this mode, the mobile station sends a proposal for the number of transmission layers (rank indication—RI) and best precoding matrix (precoding matrix index—PMI). The mobile station obtains all this feedback information based on an assessment of the channel conditions and sends its proposals to the base station. Conventionally, all this channel state information (CSI) is computed based on the signal-to-interference plus noise ratio (SINR) which would be obtained after equalization.
The SINR is then used to compute the unbound channel capacity:Ck,l=log 2(1+SINRk,l)  (3)
The basic conventional approach is summarized in the following steps:
Post-equalization SINR or capacity is directly mapped to CQI using a pre-defined mapping function or table which would typically be derived using Monte-Carlo simulation. Channel capacity or briefly ‘capacity’ is used as a metric for RI computation. The rank offering highest capacity is selected. Among all possible PMI values, the PMI is selected that maximizes post-equalization SINR or capacity. Channel capacity, or brief ‘capacity’, is generally understood in the art as the maximum amount of information, or messages, that can be communicated over a channel.
As LTE network operators are interested in a most efficient utilization of the valuable and costly resource ‘bandwidth’, there is a demand to operate the radio network at the highest possible bandwidth utilization. As a prerequisite to achieve high bandwidth efficiency, a mobile station, or user equipment (UE) in LTE terminology, must report accurate channel state information to the base station, or enhanced NodeB (eNB) in LTE terminology.
However, conventional methods for channel state information computation suffer from inaccuracy, which results in sub-optimum bandwidth efficiency. In particular when considering a large set of varying application scenarios that will exist under conditions of time and frequency selective fading and interference, the overall average bandwidth efficiency that is presently achieved is clearly lower than possible.
What is needed in the art, therefore, is a way to yield high bandwidth efficiency under both flat fading and interference conditions as well as time and frequency selective fading and interference conditions, at lower computational complexity which directly translates into lower cost, in terms of die area as well as power consumption.